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Accurate and robust tracking and reconstruction of the surgical scene is acritical enabling technology toward autonomous robotic surgery. Existingalgorithms for 3D perception in surgery mainly rely on geometric information,while we propose to also leverage semantic information inferred from theendoscopic video using image segmentation algorithms. In this paper, we presenta novel, comprehensive surgical perception framework, Semantic-SuPer, thatintegrates geometric and semantic information to facilitate data association,3D reconstruction, and tracking of endoscopic scenes, benefiting downstreamtasks like surgical navigation. The proposed framework is demonstrated onchallenging endoscopic data with deforming tissue, showing its advantages overour baseline and several other state-of the-art approaches. Our code anddataset are available at https://github.com/ucsdarclab/Python-SuPer.

Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking